Use of Space Technology for  Sustainable Development in  Iran 

Use of Space Technology for Sustainable Development in Iran Iranian Space Agency
Prepared By:
Abdolreza Ansari Amoli
In order to employ the guidelines for sustainable development, remote sensing and geographic information system (RS/GIS) have emerged as sub‐systems of space technology.
The application of these techniques in solving of ongoing environmental problems of Iran, as well as, for the future sustainable development of the country is described by reviewing some case studies conducted in Iranian Space Agency (ISA) and direct cooperation with the other companies and universities.
The Establishment of National Space Geoportal for archive data management
The general schema of the Geoportal
ISA archive content
1. . Acquired and on acquirable images from Alborz space station(High & Low Resolution )
2. Bought data (High and Mid Resolution)
Remote Sensing sub‐system
Creating a Monitoring system based on directly acquired satellite images
Currently, 8 products from MODIS satellite images, and 2 products from NOAA satellite images are daily produced . Samples of produced data that are monitored daily:
 NDVI “Normalized Difference Vegetation Index”
EVI “Enhanced Vegetation Index”
“Water Body”
 NDSI “Snow Coverage”
LST “Land Surface temperature”
SST “Sea level temperature”
 “Fire or Hot pixel” product from satellite images and alerting the specified authorities to be confirmed
Geoportal products and services
2. Remote Sensing sub‐system
NDVI “Normalized Difference Vegetation Index”
Produced daily from Terra Sensor of MODIS satellite images
Remote Sensing sub‐system
EVI “Enhanced Vegetation Index”
Produced daily from Terra Sensor of MODIS satellite images
Remote Sensing sub‐system
“Water Body”
Produced daily from Terra Sensor of MODIS satellite images
Remote Sensing sub‐system
NDSI “Snow Coverage”
Produced daily from Terra Sensor of MODIS satellite images
Snow Mapping in Iran by Using NOAA/AVHRR
Remote Sensing sub‐system
LST “Land Surface temperature”
Produced daily from Terra Sensor of MODIS satellite images
22\01\2013
Remote Sensing sub‐system
SST “Sea level temperature”
Produced daily from Terra Sensor of MODIS satellite images
22\01\2013
Caspian Sea Surface Temperature Maps 2009(September)
Monthly Caspian Sea Surface Temperature Map
2009(October)
Monthly Caspian Sea Surface Temperature Map
2009(November)
Monthly Caspian Sea Surface Temperature Map
2009(December)
Monthly Caspian Sea Surface Temperature Map
2010(Febuary)
Monthly Caspian Sea Surface Temperature Map
2011(August)
Monthly Caspian Sea Surface Temperature Map
Monthly Caspian Sea Surface Temperature
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Remote Sensing sub‐system
Fire Detected on date of 18 September in National Park in Golestan Providence
Remote Sensing sub‐system
Visual Programming and enhancement of algorithms
MODIS Process
NOAA Process
Algorithms for daily products from MODIS and NOAA satellite images have been developed, and are ready to use, accessible from menu.
Need Assessment system
How to choose a questionnaire to answer by user
Selecting desirable
questionnaire
3. Need Assessment system
Answering questions
Filling need assessment
forms by users
Oil Pollutions Monitoring in Persian Gulf
By Using Satellite Data
Drought Monitoring by Using Satellite Data
BAND1
BAND2
NDVI
January 2008
Febuary 2008
March 2008
April 2008
May 2008
June 2008
July 2008
August 2008
September 2008
October 2008
November 2008
December 2008
Annual Composition (2008)
Annual Composition‫( پ‬2009)
Annual Composition (2010)
20
Annual Composition (2011
‫پ‬
2008
2010
2009
2011
Drought Damage Map (2012)
Vegetation Index Changes During
Drought Period
Drought Damage Map
Drought Prediction by Using Artificial Neural Network
The satellite based model established in this research is able to predict and map drought intensity during the next months. Figure (1) shows a schematic diagram of the model. Satellite indices as input data are applied to an artificial neural network model and SPI (drought intensity maps) are produced as output information. Figure (1)‐ schematic diagram of the model
Difference between this research and previous works is the type of Inputs and Outputs.
In previous works ,Inputs and outputs are Meteorological and Satellite Indices , respectively.
But in this research we applied satellite images as input and we
expected to estimate SPI as output.
This research continues to get a robust model in order to predict drought. The final results showed that the best satellite based index for drought prediction by using ANN is TCI.
Also MLP is the best artificial neural network model for drought prediction.
Essential Models Satellite Indices: NDVI,NDVI‐Dev,VCI,TCI
Neural Network Models : ADALINE,MLP,RBF
Data Classification Based on Climate and Basin
NDVI
ADALINE
Dev
Climate
MLP
VCI
Basin
RBF
TCI
24 Models
Training Box of Software Designed for Modeling
Test Box of Software Designed for Modeling
Drought Prediction
Extreme Drought
Severe Drought
Moderate Drought
Mild Drought
1 year before Today 1 year before
Normal
Current Projects
Intersensor Relationship Between NOAA/AVHRR and Terra/MODIS
NOAA 1996‐2012
MODIS
2003‐2013
ISA‐UNSPIDER
Booklet:
'Considerations for Effective Use of Space Based Information to Assess
Drought at National Level ‐ Experiences from Iran'
Prepared By:
Abdolreza Ansari Amoli
Iranian Space Agency
1. Introduction …………………………………………………………………………………………6……
1‐1.Country Background………………………………………………………………………… 10…….
2.Description of the Drought Event………………………………………………………….18…….
2‐1.Drought Definition………………………………………………………………………..…..19……
2‐2.What Causes Droughts?.......................................................................21……
2‐3.The Impacts of Drought…………………………………………………………………..…25……
3.Critical data and information needed to respond to the disaster event…33…..
3‐1. Drought Management Phases……………………………………………………………36….
3‐1‐1.Drought Preparedness…………………………………………………………………….39….
3‐1‐1‐1.Drought Vulnerability Identification…………………………………………….42….
3‐1‐1‐2.Drought Prediction………………………………………………………………………43….
3‐1‐2.Drought Prevention ……………………………………………………………………….46….
3‐1‐2‐1.Drought Monitoring……………………………………………………………………46….
3‐1‐2‐2. Early Warning…………………………………………………………………………….64…
3‐1‐3. Drought Response…………………………………………………………………………68…
3‐1‐3‐1. Drought Impact (Damage) Assessment……………………………………….68..
3‐1‐3‐2. Drought Relief & Recovery Assistance………………………………………..74..
3‐1‐4).Drought Mitigation:………………………………………………………………………79..
4.Space Technology Products and Services Offered by Iranian Institutes to Manage Drought in Iran……………
4‐1. Products and Services Provided by National Institutions…………………..79.. 4‐1‐1. Major Government Organizations in Charge of Drought Management in Iran………………………………………………………………
4‐1‐1‐1. National center for drought studies (National Drought Center )…….80..
4‐1‐1‐2) Ministry of Agriculture…………………………………………………………………..88..
4‐1‐1‐2‐1. Agticultural Drought Risk Management Comprehensive Plan……..88..
4‐1‐1‐3. Isfahan Agriculture and Natural Research Center…………………………..90..
4‐1‐2. Major Research and Academic Departments Relevant to Drought in Iran
…………………………………………………………………………………………………………………..91..
4‐1‐2‐1. Shiraz Climatologic‐ Oceanography Research Center……………………..91..
4‐2. International Cooperation for Drought Management…………………………..94..
4‐2‐1. I.R. of Iran and Economic Cooperation Organization (ECO)………………..95.. 4‐3. The challenges faced while offering space based inputs and some solutions
5.Contribution of space based inputs to the decision making……………………..104.
5‐1.Project (I): “A case study for Rice Damage Assessment caused by Drought using Remote Sensing Technology –a case study in Sumea Sara, Iran”………106.
5‐2.Project(II): Drought Monitoring in Iran Using GIS/Ground Based
Datasets……………………………………………………………………………………………………..111.
5‐4.Project (IV): Drought Assessment and Monitoring for the ECO Region Using Satellite Data………………………………………………………………………………………………129.
6.Lessons Learned and Recommendations………………………………………………….133.
References………………………………………………………………………………………………….135.
Thank You!